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import os, types
import json
from enum import Enum
import requests
import time, traceback
from typing import Callable, Optional
from litellm.utils import ModelResponse, Choices, Message, Usage
import litellm
import httpx
class CohereError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
self.request = httpx.Request(method="POST", url="https://api.cohere.ai/v1/generate")
self.response = httpx.Response(status_code=status_code, request=self.request)
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
class CohereConfig():
"""
Reference: https://docs.cohere.com/reference/generate
The class `CohereConfig` provides configuration for the Cohere's API interface. Below are the parameters:
- `num_generations` (integer): Maximum number of generations returned. Default is 1, with a minimum value of 1 and a maximum value of 5.
- `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default value is 20.
- `truncate` (string): Specifies how the API handles inputs longer than maximum token length. Options include NONE, START, END. Default is END.
- `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.75.
- `preset` (string): Identifier of a custom preset, a combination of parameters such as prompt, temperature etc.
- `end_sequences` (array of strings): The generated text gets cut at the beginning of the earliest occurrence of an end sequence, which will be excluded from the text.
- `stop_sequences` (array of strings): The generated text gets cut at the end of the earliest occurrence of a stop sequence, which will be included in the text.
- `k` (integer): Limits generation at each step to top `k` most likely tokens. Default is 0.
- `p` (number): Limits generation at each step to most likely tokens with total probability mass of `p`. Default is 0.
- `frequency_penalty` (number): Reduces repetitiveness of generated tokens. Higher values apply stronger penalties to previously occurred tokens.
- `presence_penalty` (number): Reduces repetitiveness of generated tokens. Similar to frequency_penalty, but this penalty applies equally to all tokens that have already appeared.
- `return_likelihoods` (string): Specifies how and if token likelihoods are returned with the response. Options include GENERATION, ALL and NONE.
- `logit_bias` (object): Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. e.g. {"hello_world": 1233}
"""
num_generations: Optional[int]=None
max_tokens: Optional[int]=None
truncate: Optional[str]=None
temperature: Optional[int]=None
preset: Optional[str]=None
end_sequences: Optional[list]=None
stop_sequences: Optional[list]=None
k: Optional[int]=None
p: Optional[int]=None
frequency_penalty: Optional[int]=None
presence_penalty: Optional[int]=None
return_likelihoods: Optional[str]=None
logit_bias: Optional[dict]=None
def __init__(self,
num_generations: Optional[int]=None,
max_tokens: Optional[int]=None,
truncate: Optional[str]=None,
temperature: Optional[int]=None,
preset: Optional[str]=None,
end_sequences: Optional[list]=None,
stop_sequences: Optional[list]=None,
k: Optional[int]=None,
p: Optional[int]=None,
frequency_penalty: Optional[int]=None,
presence_penalty: Optional[int]=None,
return_likelihoods: Optional[str]=None,
logit_bias: Optional[dict]=None) -> None:
locals_ = locals()
for key, value in locals_.items():
if key != 'self' and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return {k: v for k, v in cls.__dict__.items()
if not k.startswith('__')
and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod))
and v is not None}
def validate_environment(api_key):
headers = {
"accept": "application/json",
"content-type": "application/json",
}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
return headers
def completion(
model: str,
messages: list,
api_base: str,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
optional_params=None,
litellm_params=None,
logger_fn=None,
):
headers = validate_environment(api_key)
completion_url = api_base
model = model
prompt = " ".join(message["content"] for message in messages)
## Load Config
config=litellm.CohereConfig.get_config()
for k, v in config.items():
if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
data = {
"model": model,
"prompt": prompt,
**optional_params,
}
## LOGGING
logging_obj.pre_call(
input=prompt,
api_key=api_key,
additional_args={"complete_input_dict": data, "headers": headers, "api_base": completion_url},
)
## COMPLETION CALL
response = requests.post(
completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
)
## error handling for cohere calls
if response.status_code!=200:
raise CohereError(message=response.text, status_code=response.status_code)
if "stream" in optional_params and optional_params["stream"] == True:
return response.iter_lines()
else:
## LOGGING
logging_obj.post_call(
input=prompt,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT
completion_response = response.json()
if "error" in completion_response:
raise CohereError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
try:
choices_list = []
for idx, item in enumerate(completion_response["generations"]):
if len(item["text"]) > 0:
message_obj = Message(content=item["text"])
else:
message_obj = Message(content=None)
choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj)
choices_list.append(choice_obj)
model_response["choices"] = choices_list
except Exception as e:
raise CohereError(message=response.text, status_code=response.status_code)
## CALCULATING USAGE
prompt_tokens = len(
encoding.encode(prompt)
)
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
model_response["created"] = int(time.time())
model_response["model"] = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens
)
model_response.usage = usage
return model_response
def embedding(
model: str,
input: list,
api_key: Optional[str] = None,
logging_obj=None,
model_response=None,
encoding=None,
optional_params=None,
):
headers = validate_environment(api_key)
embed_url = "https://api.cohere.ai/v1/embed"
model = model
data = {
"model": model,
"texts": input,
**optional_params
}
if "3" in model and "input_type" not in data:
# cohere v3 embedding models require input_type, if no input_type is provided, default to "search_document"
data["input_type"] = "search_document"
## LOGGING
logging_obj.pre_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
)
## COMPLETION CALL
response = requests.post(
embed_url, headers=headers, data=json.dumps(data)
)
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=response,
)
"""
response
{
'object': "list",
'data': [
]
'model',
'usage'
}
"""
if response.status_code!=200:
raise CohereError(message=response.text, status_code=response.status_code)
embeddings = response.json()['embeddings']
output_data = []
for idx, embedding in enumerate(embeddings):
output_data.append(
{
"object": "embedding",
"index": idx,
"embedding": embedding
}
)
model_response["object"] = "list"
model_response["data"] = output_data
model_response["model"] = model
input_tokens = 0
for text in input:
input_tokens+=len(encoding.encode(text))
model_response["usage"] = {
"prompt_tokens": input_tokens,
"total_tokens": input_tokens,
}
return model_response
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